The SVAR addon is a collection of gretl functions to estimate Structural Vector Autoregressions (SVARs) and to conduct inference on the resulting magnitudes such as the impulse response functions and short-run or long-run impact matrices. For the purpose of identifying the structural shocks short-run as well as long-run restrictions are supported, including those related to the cointegration properties in the case of non-stationary systems. For the stationary case a dialog-driven graphical interface is also offered. Inference can be based on the bootstrap, optionally using a bias correction as suggested in the literature. This documentation explains the addon's usage, capabilites and limitations, and provides some necessary econometric methodological background (version 1.32).

We clarify a point regarding the appropriate measure(s) of the variance of smoothed disturbances in the context of linear state-space models. This involves explaining how two different concepts, which are sometimes given the same name in the literature, relate to each other. We also describe the behavior of several common software packages is in this regard.

This paper presents a software package that implements Bayesian model averaging for gretl, the GNU regression, econometrics and time-series library. Bayesian model averaging is a model-building strategy that takes account of model uncertainty in conclusions about estimated parameters. It is an efficient tool for discovering the most probable models and obtaining estimates of their posterior characteristics. In recent years we have observed an increasing number of software packages devoted to Bayesian model averaging for different statistical and econometric software. In this paper, we propose the BMA package for gretl, which is an increasingly popular free, open-source software for econometric analysis with an easy-to-use graphical user interface. We introduce the BMA package for linear regression models with jointness measures proposed by Ley and Steel (2007) and Doppelhofer and Weeks (2009).

This paper presents the Gretl function package DPB for estimating dynamic binary models with panel data. The package contains routines for the estimation of the random-effects dynamic probit model proposed by Heckman (1981b) and its generalisation by Hyslop (1999) and Keane and Sauer (2009) to accommodate AR(1) disturbances. The fixed-effects estimator by Bartolucci and Nigro (2010) is also implemented. DPB is available on the Gretl function packages archive.